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Published June 25, 2021 | Version v1
Journal article Open

A global model of bird detection in high resolution airborne images using computer vision

  • 1. University of Florida
  • 2. Nature Conservancy
  • 3. University of New Mexico
  • 4. The Nature Conservancy
  • 5. University of Oxford
  • 6. University College Cork
  • 7. Monash University
  • 8. Museum and Institute of Zoology, Polish Academy of Sciences
  • 9. Quantearo, NV

Description

Bird Detection Datasets

Each dataset is organized into train and test splits, generally with 90% of images in train. Whereever possible the train/test split does not cross individual flights or locations. 

The general format is a csv with the columns: image_path, xmin, xmax, ymin, ymax, label

The coordinates relative to the image origin, there is no geographic projection in the images.

Bird Detection Models

 Using https://github.com/weecology/BirdDetector and the deepforest python package https://deepforest.readthedocs.io/. 

A single model for future use was trained using all training and test data together. (Bird.pt). 

Using the deepforest python package

```

from deepforest import main

import torch

m = main.deepforest()

m.model.load_state_dict(torch.load(<path to .pt>))

```

More information can found [biorxiv link].

Files

everglades.zip

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